1 citations · 3 across the 14 of their papers we have counts for
6 papers · 1 filter
A minimalistic representation model for head direction system
Minglu Zhao, Dehong Xu, Deqian Kong +2
We present a minimalistic representation model for the head direction (HD) system, aiming to learn a high-dimensional representation of head direction that captures essential prope…
DODT: Enhanced Online Decision Transformer Learning through Dreamer's Actor-Critic Trajectory Forecasting
Eric Hanchen Jiang, Zhi Zhang, Dinghuai Zhang +9
Advancements in reinforcement learning have led to the development of sophisticated models capable of learning complex decision-making tasks. However, efficiently integrating world…
Latent Space Energy-based Neural ODEs
Sheng Cheng, Deqian Kong, Jianwen Xie +3
This paper introduces novel deep dynamical models designed to represent continuous-time sequences. Our approach employs a neural emission model to generate each data point in the t…
"Noisier" Noise Contrastive Eestimation is (Almost) Maximum Likelihood
Peiyu Yu, Dinghuai Zhang, Hengzhi He +10
Noise Contrastive Estimation (NCE) has fueled major breakthroughs in representation learning and generative modeling. Yet a long-standing challenge remains: accurately estimating r…
Molecule Design by Latent Prompt Transformer
Deqian Kong, Yuhao Huang, Jianwen Xie +8
This work explores the challenging problem of molecule design by framing it as a conditional generative modeling task, where target biological properties or desired chemical constr…
Latent Plan Transformer for Trajectory Abstraction: Planning as Latent Space Inference
Deqian Kong, Dehong Xu, Minglu Zhao +6
In tasks aiming for long-term returns, planning becomes essential. We study generative modeling for planning with datasets repurposed from offline reinforcement learning. Specifica…